Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington’s Disease

作者： Dan Wu1, Andreia V. Faria1, Laurent Younes2,3,4, Christopher A. Ross5, Susumu Mori1,6, Michael I. Miller2,3,7 1The Russell H. Morgan Department of Radiology and Radiological Science,Johns Hopkins University School of Medicine, 2Center for Imaging Science,Johns Hopkins University, 3Institute for Computational Medicine,Johns Hopkins University, 4Department of Applied Mathematics and Statistics,Johns Hopkins University, 5Division of Neurobiology, Departments of Psychiatry, Neurology, Neuroscience and Pharmacology, and Program in Cellular and Molecular Medicine,Johns Hopkins University School of Medicine, 6F.M. Kirby Research Center for Functional Brain Imaging,Kennedy Krieger Institute, 7Department of Biomedical Engineering,Johns Hopkins University

简介：

Overview

This study presents a statistical model for analyzing volumetric MRI data to identify the onset of brain atrophy in premanifest Huntington's disease. The method utilizes an atlas-based segmentation pipeline to achieve whole-brain mapping of change-points in brain volumes.

Key Study Components

Area of Science

• Neuroscience
• Neuroimaging
• Neurodegenerative Diseases

Background

• Quantitative analysis of brain anatomical changes is crucial in understanding neurodegenerative diseases.
• Identifying change-points in brain atrophy can provide insights into disease progression.
• Huntington's disease serves as a model for studying brain atrophy patterns.
• Atlas-based segmentation enhances the accuracy of volumetric analysis.

Purpose of Study

• To develop a method for detecting critical change-points in brain atrophy.
• To provide a systematic view of disease progression across the brain.
• To utilize automated image segmentation for efficient analysis.

Methods Used

• Volumetric analysis of T1-weighted MR images.
• Atlas-based segmentation pipeline for brain volume measurement.
• Change-point analysis to identify onset of atrophy.
• Utilization of MRICloud for automated image processing.

Main Results

• Successful identification of change-points in brain atrophy.
• Mapping of atrophy patterns across the whole brain.
• Demonstration of the method's effectiveness in large population studies.
• Provision of unique spatial-temporal information regarding brain degeneration.

Conclusions

• The proposed method offers a robust approach to studying neurodegenerative diseases.
• It enhances understanding of brain atrophy dynamics in Huntington's disease.
• The combination of segmentation and change-point analysis is valuable for future research.

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